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scikit-learn is a widely-used Python module for classic machine learning. It is built on top of SciPy.

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donnemartin / data-science-ipython-notebooks

Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

python aws data-science machine-learning caffe theano big-data spark deep-learning hadoop tensorflow numpy scikit-learn keras pandas kaggle scipy matplotlib mapreduce
  • Updated Oct 12, 2022
  • Python

TarrySingh / Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials

A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Climate / Energy, Automotives, Retail, Pharma, Medicine, Healthcare, Policy, Ethics and more.

python aws machine-learning lua deep-learning neural-network tensorflow scikit-learn keras ipython-notebook torch pandas pytorch kaggle artificial-intelligence matplotlib tensorflow-tutorials convolutional-neural-networks python-data capsule-network
  • Updated Nov 24, 2022
  • Python
igel

nidhaloff / igel

Sponsor

a delightful machine learning tool that allows you to train, test, and use models without writing code

data-science machine-learning automation neural-network scikit-learn sklearn machine-learning-algorithms artificial-intelligence neural-networks data-analysis machine-learning-library machinelearning preprocessing hacktoberfest automl scikitlearn-machine-learning automl-experiments hacktoberfest2021
  • Updated Feb 7, 2022
  • Python
mljar-supervised

mljar / mljar-supervised

Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

data-science machine-learning neural-network random-forest scikit-learn xgboost hyperparameter-optimization lightgbm ensemble feature-engineering decision-tree hyper-parameters automl automated-machine-learning catboost automatic-machine-learning mljar shap tuning-algorithm models-tuning
  • Updated Nov 3, 2022
  • Python

microsoft / FLAML

A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.

python data-science machine-learning natural-language-processing deep-learning random-forest scikit-learn jupyter-notebook tabular-data regression tuning hyperparameter-optimization classification natural-language-generation automl automated-machine-learning finetuning timeseries-forecasting hyperparam
  • Updated Nov 30, 2022
  • Jupyter Notebook

dipanjanS / practical-machine-learning-with-python

Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system.

python machine-learning natural-language-processing computer-vision deep-learning jupyter notebook clustering tensorflow scikit-learn keras jupyter-notebook pandas spacy nltk classification convolutional-neural-networks prophet statsmodels time-series-analysis
  • Updated Oct 1, 2020
  • Jupyter Notebook
text-analytics-with-python

dipanjanS / text-analytics-with-python

Learn how to process, classify, cluster, summarize, understand syntax, semantics and sentiment of text data with the power of Python! This repository contains code and datasets used in my book, "Text Analytics with Python" published by Apress/Springer.

python semantic natural-language-processing sentiment-analysis text-classification clustering pattern natural-language scikit-learn sentiment spacy nltk text-summarization gensim stanford-nlp text-analytics
  • Updated Dec 25, 2020
  • Jupyter Notebook
Otto

KartikChugh / Otto

Otto makes machine learning an intuitive, natural language experience. 🏆 Facebook AI Hackathon winner ⭐️ #1 Trending on MadeWithML.com ⭐️ #4 Trending JavaScript Project on GitHub ⭐️ #15 Trending (All Languages) on GitHub

python machine-learning deep-learning scikit-learn artificial-intelligence trending-repositories chat-application automl keras-tensorflow natural-language-understanding
  • Updated Nov 14, 2022
  • JavaScript
Neuraxle

Neuraxio / Neuraxle

The world's cleanest AutoML library ✨ - Do hyperparameter tuning with the right pipeline abstractions to write clean deep learning production pipelines. Let your pipeline steps have hyperparameter spaces. Design steps in your pipeline like components. Compatible with Scikit-Learn, TensorFlow, and most other libraries, frameworks and MLOps environments.

machine-learning framework deep-learning pipeline scikit-learn python-library parallel pipeline-framework hyperparameters hyperparameter-optimization hyperparameter-tuning hyperparameter-search neuraxle
  • Updated Aug 17, 2022
  • Python
Hyperactive

SimonBlanke / Hyperactive

An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.

python data-science machine-learning deep-learning optimization scikit-learn parallel-computing keras pytorch model-selection xgboost hyperparameter-optimization feature-engineering bayesian-optimization automated-machine-learning parameter-tuning neural-architecture-search hyperactive
  • Updated Nov 27, 2022
  • Python

richmondu / libfaceid

libfaceid is a research framework for prototyping of face recognition solutions. It seamlessly integrates multiple detection, recognition and liveness models w/ speech synthesis and speech recognition.

raspberry-pi opencv machine-learning deep-learning tensorflow scikit-learn keras speech-synthesis speech-recognition face-recognition face-detection dlib openface facenet convolutional-neural-network pose-estimation emotion-detection gender-detection age-detection face-spoofing-detection
  • Updated Nov 22, 2022
  • Python

ashishpatel26 / Amazing-Feature-Engineering

Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.

data-science machine-learning data-mining deep-learning scikit-learn data-visualization feature-selection feature-extraction data-analysis data-scientists feature-engineering features feature-scaling
  • Updated Nov 29, 2020
  • Jupyter Notebook
explainx

explainX / explainx

Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code. We are looking for co-authors to take this project forward. Reach out @ ms8909@nyu.edu

machine-learning scikit-learn transparency blackbox bias interpretability explainable-artificial-intelligence interpretable-ai explainable-ai explainable-ml xai interpretable-machine-learning machine-learning-interpretability explainability aws-sagemaker explainx
  • Updated Sep 16, 2022
  • Jupyter Notebook
abess

abess-team / abess

Fast Best-Subset Selection Library

python machine-learning r linear-regression scikit-learn high-dimensional-data feature-selection logistic-regression cox-regression principal-component-analysis classification-algorithm ordinal-regression poisson-regression sure-independence-screening multitask-learning sparse-principal-component-analysis robust-principal-component-analysis polynomial-algorithm best-subset-selection
  • Updated Nov 21, 2022
  • C++
skforecast

JoaquinAmatRodrigo / skforecast

Time series forecasting with scikit-learn models

python data-science machine-learning time-series scikit-learn forecasting series-forecasting series-temporales direct-forecasting autoregressive-forecasters multi-step-forecasters exogenous-predictors multi-series-forecasting multi-step-forecasting backtesting-forecasters quantile-forecasting multiple-time-series-forecasting weighted-time-series-forecasting
  • Updated Nov 30, 2022
  • Jupyter Notebook

giacbrd / ShallowLearn

An experiment about re-implementing supervised learning models based on shallow neural network approaches (e.g. fastText) with some additional exclusive features and nice API. Written in Python and fully compatible with Scikit-learn.

machine-learning text-mining neural-network text-classification word2vec scikit-learn word-embeddings supervised-learning gensim fasttext online-learning shallow-learning
  • Updated Aug 9, 2017
  • Python

rodrigo-arenas / Sklearn-genetic-opt

Hyperparameters tuning and feature selection, using evolutionary algorithms.

python machine-learning scikit-learn sklearn feature-selection artificial-intelligence hyperparameters model-selection hyperparameter-optimization evolutionary-algorithms help-wanted looking-for-contributors up-for-grabs automl deap contributions-welcome good-first-issue goodfirstissue begginer-friendly featureselection
  • Updated Oct 26, 2022
  • Python
imbalanced-ensemble

ZhiningLiu1998 / imbalanced-ensemble

Class-imbalanced / Long-tailed ensemble learning in Python. Modular, flexible, and extensible. | 模块化、灵活、易扩展的类别不平衡/长尾机器学习库

python data-science machine-learning data-mining scikit-learn sklearn python3 classification ensemble ensemble-learning class-imbalance ensemble-model imbalanced-data imbalanced-learning long-tail multi-class-classification imbalanced-classification ensemble-imbalanced-learning
  • Updated Oct 2, 2022
  • Python

zama-ai / concrete-ml

Concrete-ML is a Privacy-Preserving Machine Learning (PPML) open-source set of tools which aims to simplify the use of fully homomorphic encryption (FHE) for data scientists. Particular care was given to the simplicity of our Python package in order to make it usable by any data scientist, even those without prior cryptography knowledge.

python data-science machine-learning privacy scikit-learn torch homomorphic-encryption fhe ppml tfhe
  • Updated Nov 28, 2022
  • Python

upgini / upgini

Free automated data enrichment library for machine learning → searches through thousands of ready-to-use features from public and community shared data sources

data-science machine-learning scikit-learn python-library kaggle feature-selection open-data feature-extraction kaggle-competition public-data feature-engineering features data-scraping automl low-code data-enrichment kaggle-solution automated-feature-engineering pycaret
  • Updated Nov 30, 2022
  • Python
nlp_workshop_odsc_europe20

dipanjanS / nlp_workshop_odsc_europe20

Extensive tutorials for the Advanced NLP Workshop in Open Data Science Conference Europe 2020. We will leverage machine learning, deep learning and deep transfer learning to learn and solve popular tasks using NLP including NER, Classification, Recommendation \ Information Retrieval, Summarization, Classification, Language Translation, Q&A and Topic Models.

python machine-learning natural-language-processing deep-learning tensorflow scikit-learn jupyter-notebook transformers pytorch spacy nltk gensim transfer-learning
  • Updated Sep 18, 2020
  • Jupyter Notebook

ditikrushna / End-to-End-Diabetes-Prediction-Application-Using-Machine-Learning

In this project, the objective is to predict whether the person has Diabetes or not based on various features like Number of Pregnancies, Insulin Level, Age, BMI.

heroku flask machine-learning deployment scikit-learn machine-learning-algorithms health-data mini-projects end end-to-end-machine-learning diabetes-prediction mini-project-on-machine-learning machine-learnin-model-deplyment end-end-machine-learning-project machine-learning-mini-project
  • Updated Apr 27, 2022
  • HTML

ani8897 / Human-Activity-Recognition

Classifying the physical activities performed by a user based on accelerometer and gyroscope sensor data collected by a smartphone in the user’s pocket. The activities to be classified are: Standing, Sitting, Stairsup, StairsDown, Walking and Cycling.

machine-learning scikit-learn gyroscope accelerometer rnn human-activity-recognition sensor-data keras-neural-networks lstm-neural-networks downsampling-data
  • Updated Jun 23, 2017
  • Python
Machine-Learning

anubhavshrimal / Machine-Learning

The projects I do in Machine Learning with PyTorch, keras, Tensorflow, scikit learn and Python.

machine-learning deep-neural-networks reinforcement-learning deep-learning tensorflow scikit-learn python3 pytorch lstm kaggle-competition neural-networks self-driving-car image-captioning face-recognition gans chess-ai quick-draw machine-learning-projects sagemaker-deployment malaria-detection
  • Updated Oct 2, 2020
  • Jupyter Notebook

danmorales / CursoDS_ProfDanilo

Códigos Python com diferentes aplicações como técnicas de machine learning e deep learning, fundamentos de estatística, problemas de regressão de classificação. Os vídeos com as explicações teóricas estão disponíveis no meu canal do YouTube

data-science machine-learning deep-learning tensorflow numpy pandas-dataframe scikit-learn machine-learning-algorithms aprendizado-de-maquina scipy tensorflow-tutorials keras-models keras-classification-models keras-layer keras-neural-networks scikitlearn-machine-learning ciencia-de-dados pandas-python
  • Updated Oct 19, 2020
  • Jupyter Notebook

girishp92 / Human-activity-recognition-using-Recurrent-Neural-Nets-RNN-LSTM-and-Tensorflow-on-Smartphones

This was my Master's project where i was involved using a dataset from Wireless Sensor Data Mining Lab (WISDM) to build a machine learning model to predict basic human activities using a smartphone accelerometer, Using Tensorflow framework, recurrent neural nets and multiple stacks of Long-short-term memory units(LSTM) for building a deep network. After the model was trained, it was saved and exported to an android application and the predictions were made using the model and the interface to speak out the results using text-to-speech API.

machine-learning deep-learning proof-of-concept scikit-learn android-application python3 pickle rnn-tensorflow androidstudio lstm-neural-networks pycharm-ide human-activities hidden-units smartphone-accelerometer
  • Updated Sep 26, 2017
  • Python

davisidarta / topometry

A comprehensive dimensional reduction framework to recover the latent topology from high-dimensional data.

visualization data-science machine-learning clustering graphs scikit-learn data-visualization information-visualization dimensionality-reduction topology-visualiztion dimension-reduction topological-data-analysis knowledge-representation topology-optimization single-cell-genomics single-cell-analysis single-cell-omics data-topology
  • Updated Nov 29, 2022
  • Python

habedi / PracticalMachineLearning

A collection of ML related stuff including notebooks, codes and a curated list of various useful resources such as books and softwares. Almost everything mentioned here is free (as speech not free food) or open-source.

python data-science machine-learning data-mining deep-neural-networks deep-learning graph-algorithms scikit-learn jupyter-notebook pandas kaggle artificial-intelligence data-analysis datasets python-machine-learning graph-mining self-learning anomaly-detection zeppelin-notebook data-science-resourses
  • Updated Nov 18, 2022
  • Jupyter Notebook

subho406 / TF-Speech-Recognition-Challenge-Solution

Source code of the model used in Tensorflow Speech Recognition Challenge (https://www.kaggle.com/c/tensorflow-speech-recognition-challenge). The solution ranked in top 5% in private leaderboard.

raspberry-pi deep-learning neural-network tensorflow scikit-learn speech recurrent-neural-networks speech-recognition ensemble-learning convolutional-neural-networks audio-recognition
  • Updated Mar 31, 2018
  • Jupyter Notebook
Stock_Market_Data_Analysis

jcwill415 / Stock_Market_Data_Analysis

Scrape, analyze & visualize stock market data for the S&P500 using Python. Build a basic trading strategy using machine learning to assess company performance and determine buy, sell, hold. Read me & instructions available in Spanish. This is a working repo, with plans to expand the project from technical analysis to fundamental analysis.

machine-learning numpy scikit-learn pandas data-visualization python3 stock-market matplotlib data-manipulation investment-analysis beautifulsoup4 sp500-real-time-data investment-decisions stock-price-data
  • Updated Aug 7, 2020
  • Jupyter Notebook

AFAgarap / wisconsin-breast-cancer

[ICMLSC 2018] On Breast Cancer Detection: An Application of Machine Learning Algorithms on the Wisconsin Diagnostic Dataset

machine-learning tensorflow linear-regression scikit-learn machine-learning-algorithms supervised-learning classification logistic-regression support-vector-machine softmax-regression binary-classification nearest-neighbours-classifier multilayer-perceptron recurrent-neural-network
  • Updated Nov 22, 2022
  • Python

ptyadana / Data-Science-and-Machine-Learning-Projects-Dojo

collections of data science, machine learning and data visualization projects with pandas, sklearn, matplotlib, tensorflow2, Keras, various ML algorithms like random forest classifier, boosting, etc

data-science machine-learning natural-language-processing deep-learning tensorflow scikit-learn machine-learning-algorithms keras pandas data-visualization seaborn data-analysis boosting-algorithms probability-statistics
  • Updated Apr 4, 2022
  • Jupyter Notebook
Machine-Learning-with-Scikit-Learn-Python-3.x

reddyprasade / Machine-Learning-with-Scikit-Learn-Python-3.x

In general, a learning problem considers a set of n samples of data and then tries to predict properties of unknown data. If each sample is more than a single number and, for instance, a multi-dimensional entry (aka multivariate data), it is said to have several attributes or features. Learning problems fall into a few categories: supervised learning, in which the data comes with additional attributes that we want to predict (Click here to go to the scikit-learn supervised learning page).This problem can be either: classification: samples belong to two or more classes and we want to learn from already labeled data how to predict the class of unlabeled data. An example of a classification problem would be handwritten digit recognition, in which the aim is to assign each input vector to one of a finite number of discrete categories. Another way to think of classification is as a discrete (as opposed to continuous) form of supervised learning where one has a limited number of categories and for each of the n samples provided, one is to try to label them with the correct category or class. regression: if the desired output consists of one or more continuous variables, then the task is called regression. An example of a regression problem would be the prediction of the length of a salmon as a function of its age and weight. unsupervised learning, in which the training data consists of a set of input vectors x without any corresponding target values. The goal in such problems may be to discover groups of similar examples within the data, where it is called clustering, or to determine the distribution of data within the input space, known as density estimation, or to project the data from a high-dimensional space down to two or three dimensions for the purpose of visualization (Click here to go to the Scikit-Learn unsupervised learning page).

python machine-learning reinforcement-learning vector scikit-learn sklearn machine-learning-algorithms scikit-image regression prediction consists supervised-learning classification semi-supervised-learning python-3 unsupervised-learning scikit-model scikit-learn-api scikit-learn-python
  • Updated Jun 17, 2021
  • Jupyter Notebook

PacktWorkshops / The-Deep-Learning-with-Keras-Workshop

An Interactive Approach to Understanding Deep Learning with Keras

machine-learning tensorflow scikit-learn keras cross-validation regression classification artificial-neural-networks logistic-regression regularization support-vector-machine vectors decision-trees hyperparameter-tuning model-evaluation magnetic-resonance-imaging k-means-clustering model-tuning scalars linear-transformation
  • Updated Nov 22, 2022
  • Jupyter Notebook

jim-spyropoulos / Trajectory-Analysis-and-Classification-in-Python-Pandas-and-Scikit-Learn

Formed trajectories of sets of points.Experimented on finding similarities between trajectories based on DTW (Dynamic Time Warping) and LCSS (Longest Common SubSequence) algorithms.Modeled trajectories as strings based on a Grid representation.Benchmarked KNN, Random Forest, Logistic Regression classification algorithms to classify efficiently trajectories.

python machine-learning random-forest dtw scikit-learn classification logistic-regression similarity-measures trajectory-analysis knn trajectory scikitlearn-machine-learning classifiers
  • Updated Jun 22, 2022
  • Python

IliaZenkov / sklearn-audio-classification

An in-depth analysis of audio classification on the RAVDESS dataset. Feature engineering, hyperparameter optimization, model evaluation, and cross-validation with a variety of ML techniques and MLP

audio machine-learning deep-neural-networks scikit-learn sklearn emotion classification audio-data feature-engineering deep-learning-tutorial model-evaluation emotion-detection emotion-recognition machine-learning-tutorials mlp-model ravdess-dataset dnns
  • Updated Nov 6, 2020
  • Jupyter Notebook
Cryptocurrency-Prediction-with-Artificial-Intelligence-V3.0-GRU-Neural-Network

emirhanai / Cryptocurrency-Prediction-with-Artificial-Intelligence-V3.0-GRU-Neural-Network

Cryptocurrency Prediction with Artificial Intelligence V3.0 [GRU Neural Network]

machine-learning crypto deep-learning neural-network bitcoin tensorflow scikit-learn sklearn keras artificial-intelligence cryptocurrency gru neural-networks artificial-neural-networks btc cryptocurrencies artificial-intelligence-algorithms usdt binance tensorflow2
  • Updated Mar 29, 2022
  • Jupyter Notebook

kaushikjadhav01 / Movie-Recommendation-Chatbot

Movie Recommendation Chatbot provides information about a movie like plot, genre, revenue, budget, imdb rating, imdb links, etc. The model was trained with Kaggle’s movies metadata dataset. To give a recommendation of similar movies, Cosine Similarity and TFID vectorizer were used. Slack API was used to provide a Front End for the chatbot. IBM Watson was used to link the Python code for Natural Language Processing with the front end hosted on Slack API. Libraries like nltk, sklearn, pandas and nlp were used to perform Natural Language Processing and cater to user queries and responses.

python slack natural-language-processing watson scikit-learn chatbot pandas artificial-intelligence recommendation-engine movie-recommendation tabulate
  • Updated Jun 27, 2020
  • Jupyter Notebook

lgaalves / AppliedMathSchoolLectures

Lectures on "crime and political corruption analysis using data mining, machine learning and complex networks" at the School of Applied Mathematics in the Institute of Mathematics and Computer Science at University of São Paulo

python data-science machine-learning data-mining big-data social-networks scikit-learn jupyter-notebook community-detection networkx web-scraping complex-networks igraph crime-prediction corruption-networks crimonology quantitative-criminology
  • Updated Jul 7, 2019
  • Jupyter Notebook

TatevKaren / recurrent-neural-network-pricing-model

Price Prediction Case Study predicting the Bitcoin price and the Google stock price using Deep Learning, RNN with LSTM layers with TensorFlow and Keras in Python. (Includes: Data, Case Study Paper, Code)

machine-learning google deep-learning bitcoin tensorflow scikit-learn keras pandas recurrent-neural-networks lstm stock-price-prediction stock-prices rnn-model bitcoin-price price-prediction google-stock-prices case-study-paper
  • Updated Apr 19, 2022
  • Python

BobbyWilt / Spotify_Song_Recommender

This project leverages spotify's api and provided user playlists to create and tune a neural network model that generates song recommendations based off of song data in provided playlists.

python api spotify machine-learning neural-network tensorflow scikit-learn plotly keras pandas hyperparameter-optimization recommendation-system regularization feature-engineering custom-metrics bayesian-optimization spotipy song-analysis
  • Updated Jun 24, 2022
  • Jupyter Notebook

alexfrancow / CVE-Search

CVE-Search (name still in alpha), is a Machine Learning tool focused on the detection of exploits or proofs of concept in social networks such as Twitter, Github. It is also capable of doing related searches on Google, Yandex, DuckDuckGo on CVEs and detecting if the content may be a functional exploit, a proof of concept or simply information about the vulnerability.

flask machine-learning scikit-learn postgresql pandas python3 exploits poc cve nlp-machine-learning
  • Updated Jan 6, 2021
  • Jupyter Notebook

rehmanzafar / dlime_experiments

In this work, we propose a deterministic version of Local Interpretable Model Agnostic Explanations (LIME) and the experimental results on three different medical datasets shows the superiority for Deterministic Local Interpretable Model-Agnostic Explanations (DLIME).

machine-learning random-forest scikit-learn python3 neural-networks clusters knn lime classifiers explainable-ai explainable-ml xai heirarchical-clustering breast-cancer-dataset dlime
  • Updated Jun 22, 2022
  • Jupyter Notebook

vikrantarora25 / Car-Price-Prediction-Highly-Comprehensive-Linear-Regression-Project-

A Linear Regression model to predict the car prices for the U.S market to help a new entrant understand important pricing variables in the U.S automobile industry. A highly comprehensive analysis with detailed explanation of all steps; data cleaning, exploration, visualization, feature selection, model building, evaluation & MLR assumptions validity.

python data-science machine-learning linear-regression scikit-learn sklearn cross-validation regression seaborn matplotlib outlier-detection recursive-algorithm regression-models outlier-removal regression-algorithms regression-analysis price-prediction kfold-cross-validation recursive-feature-elimination rfe
  • Updated Jul 28, 2020
  • Jupyter Notebook

thiagomarquesrocha / Kaio-machine-learning-human-face-detection

Machine Learning project a case study focused on the interaction with digital characters, using a character called "Kaio", which, based on the automatic detection of facial expressions and classification of emotions, interacts with humans by classifying emotions and imitating expressions

javascript android python java machine-learning django notebook percentage detection scikit-learn sklearn jupyter-notebook smile supervised-learning eyes blink mobile-vision human-faces
  • Updated May 18, 2018
  • Jupyter Notebook

Yeaseen / ML_Pattern

🔱 Some recognized algorithms[Decision Tree, Adaboost, Perceptron, Clustering, Neural network etc. ] of machine learning and pattern recognition are implemented from scratch using python. Data sets are also included to test the algorithms.

python template-matching numpy scikit-learn pocket basic-learning artificial-neural-networks scratch decision-tree-classifier multiclass-classification recommendation-algorithm perceptron-learning-algorithm binary-perceptron adaboost-learning reward-and-punishment multiclass-kersel als-algorithm
  • Updated May 9, 2019
  • Python

Seyed-Ali-Ahmadi / Python-for-Remote-Sensing

python codes for remote sensing applications will be uploaded here. I will try to teach everything I learn during my projects in here.

python machine-learning deep-learning scikit-learn point-cloud gdal remote-sensing lidar satellite-imagery satellite-data hyperspectral-image-classification multispectral-images satellite-images hyperspectral-imaging lidar-point-cloud pdal gdal-python spacenet-dataset gdal-python-libraries python-pdal
  • Updated Feb 13, 2022
  • Jupyter Notebook

sourcecode369 / ml-algorithms-on-scikit-and-keras

Implementation scripts of Machine Learning algorithms on Scikit-learn and Keras for complete novice..

machine-learning natural-language-processing reinforcement-learning deep-learning clustering numpy scikit-learn keras regression pandas xgboost classification dimensionality-reduction matplotlib t-sne data-preprocessing grid-search association-rule-learning kfold-cross-validation multiarm-bandit
  • Updated Jul 22, 2018
  • Jupyter Notebook

ritabratamaiti / ASD-ML-API

This project has 3 goals: To find out the best machine learning pipeline for predicting ASD cases using genetic algorithms, via the TPOT library. (Classification Problem) Compare the accuracy of the accuracy of the determined pipeline, with a standard Naive-Bayes classifier. Saving the classifier as an external file, and use this file in a Flask API to make predictions in the cloud.

machine-learning genetic-algorithm scikit-learn flask-api tpot
  • Updated May 28, 2020
  • Rich Text Format

chicago-joe / Python-ML-for-Financial-Applications

Moody's Bond Rating Classifier and USPHCI Economic Activity Forecast Modeling

python data-science machine-learning scikit-learn machine-learning-algorithms regression feature-extraction classification data-analysis ensemble-learning regression-models hyperparameter-tuning financial-markets classification-algorithm financial-engineering classification-trees regression-trees regression-algorithms classification-model machinelearning-python
  • Updated Jun 13, 2019
  • Python

zainsiddiqui / Predicting-Survival-on-Titanic-with-Machine-Learning-and-Graphical-User-Interface

This program consists of clean and polished Graphical User Interface (GUI) that interacts with 8 Machine Learning models and data visualization tools through the use of different Python libraries. The user can interact with the GUI through selecting which model to run on the testing data on, which then takes them to a screen displaying the prediction results of the testing data as well as the general model accuracy. The screen also includes various buttons that, when selected, display complex and attractive data visualizations on the testing data.

data-science machine-learning gui numpy scikit-learn machine-learning-algorithms data-visualization python3 graphical-user-interface titanic-survival-prediction tkinter-gui
  • Updated May 22, 2019
  • Python

ritabratamaiti / Blooddonorprediction

Thanks to digitization, we often have access to large databases, consisting of various fields of information, ranging from numbers to texts and even boolean values. Such databases lend themselves especially well to machine learning, classification and big data analysis tasks. We are able to train classifiers, using already existing data and use them for predicting the values of a certain field, given that we have information regarding the other fields. Most specifically, in this study, we look at the Electronic Health Records (EHRs) that are compiled by hospitals. These EHRs are convenient means of accessing data of individual patients, but there processing as a whole still remains a task. However, EHRs that are composed of coherent, well-tabulated structures lend themselves quite well to the application to machine language, via the usage of classifiers. In this study, we look at a Blood Transfusion Service Center Data Set (Data taken from the Blood Transfusion Service Center in Hsin-Chu City in Taiwan). We used scikit-learn machine learning in python. From Support Vector Machines(SVM), we use Support Vector Classification(SVC), from the linear model we import Perceptron. We also used the K.neighborsclassifier and the decision tree classifiers. We segmented the database into the 2 parts. Using the first, we trained the classifiers and the next part was used to verify if the classifier prediction matched that of the actual values.

classifier machine-learning scikit-learn support-vector-machine ehr decision-tree-classifier tpot
  • Updated Aug 5, 2018
  • Python

Ferdib-Al-Islam / detect-parkinsons-disease

Parkinson disease is associated with movement disorder symptoms, such as tremor, rigidity, bradykinesia, and postural instability. The manifestation of bradykinesia and rigidity is often in the early stages of the disease. These have a noticeable effect on the handwriting and sketching abilities of patients, and micrographia has been used for early-stage diagnosis of Parkinson’s disease. While handwriting of a person is influenced by a number of factors such as language proficiency and education, sketching of a shape such as the spiral has been found to be non-invasive and independent measure.

python random-forest scikit-learn sklearn parkinsons-disease
  • Updated Dec 20, 2019
  • Python
Home-Credit-Default-Risk-Recognition

abhishekdbihani / Home-Credit-Default-Risk-Recognition

The project provides a complete end-to-end workflow for building a binary classifier in Python to recognize the risk of housing loan default. It includes methods like automated feature engineering for connecting relational databases, comparison of different classifiers on imbalanced data, and hyperparameter tuning using Bayesian optimization.

scikit-learn feature-selection xgboost hyperparameter-optimization lightgbm hyperopt feature-engineering bayesian-optimization hyperparameter-tuning binary-classification imbalanced-data automl loan-default-prediction udacity-machine-learning-nanodegree f1-score automated-feature-engineering featuretools auc-roc-curve auc-roc-score k-fold-cross-validation
  • Updated Jul 2, 2020
  • Jupyter Notebook

raj1603chdry / Fake-News-Detection-System

Fake News Detection System for detecting whether news is fake or not. The model is trained using "Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection. Link for dataset: https://arxiv.org/abs/1705.00648.

machine-learning pipeline random-forest scikit-learn pattern-library word-cloud nltk naive-bayes-classifier logistic-regression data-preprocessing fake-news pos-tagging svm-classifier count-vectorizer custom-transformer voting-classifier gridsearchcv tfidf-vectorizer
  • Updated Jan 25, 2020
  • Jupyter Notebook

ashish-kamboj / Data-Science

EDA and Machine Learning Models in R and Python (Regression, Classification, Clustering, SVM, Decision Tree, Random Forest, Time-Series Analysis, Recommender System, XGBoost)

visualization python ggplot2 r random-forest clustering numpy svm linear-regression scikit-learn exploratory-data-analysis eda pandas xgboost logistic-regression recommender-system decision-trees gradio time-series-analysis pycaret
  • Updated Jun 2, 2022
  • Jupyter Notebook

MBKraus / dockerized_data_science_playground

Multi-docker container data science / engineering playground (w/ Kafka, Airflow, MLFlow, Tensorflow-Keras / SKLearn) for simulating a microservices-oriented architecture

docker data-science airflow kafka docker-compose tensorflow scikit-learn sklearn postgresql keras zookeeper datascience data-engineering kafka-consumer kafka-producer kafka-python mlflow mlflow-tracking-server
  • Updated Nov 22, 2022
  • Dockerfile
Vehicle-detection-using-deep-learning-with-Tensorflow-and-Python

ashar367 / Vehicle-detection-using-deep-learning-with-Tensorflow-and-Python

This repository shows how to train a CNN model for detecting vehicles and other objects on streets

visualization machine-learning computer-vision deep-learning neural-network scikit-learn image-classification face-recognition face-detection tensorflow-tutorials imageprocessing convolutional-neural-networks object-detection object-tracking object-recognition gesture-recognition tensorflow-models scikitlearn-machine-learning tensorflow-examples uav-images
  • Updated Nov 22, 2019
  • Python

suneelpatel / Python-for-Data-Scientists

This repository will offer several learning strategies and advanced study material along with the interesting use case and programs that will help jump start your journey of becoming a data scientist with python !!!

opencv data-science computer-vision numpy scikit-learn python-library pandas data-visualization scipy data-analysis matplotlib webscraping python-package webmapping python-for-data-scientists
  • Updated Sep 2, 2019
  • Jupyter Notebook

ikigai-aa / Face-Mask-Detector-using-MobileNetV2

This is a simple image classification project trained on the top of Keras/Tensorflow API with MobileNetV2 deep neural network architecture having weights considered as pre-trained 'imagenet' weights. The trained model (mask-detector-model.h5) takes the real-time video from webcam as an input and predicts if the face landmarks in Region of Interest (ROI) is 'Mask' or 'No Mask' with real-time on screen accuracy.

python opencv machine-learning deep-learning scikit-learn jupyter-notebook keras-tensorflow mobilenet coronavirus face-mask-detector
  • Updated Nov 22, 2022
  • Jupyter Notebook

ikigai-aa / Face-Mask-Detector-using-MobileNetV2

This is a simple image classification project trained on the top of Keras/Tensorflow API with MobileNetV2 deep neural network architecture having weights considered as pre-trained 'imagenet' weights. The trained model (mask-detector-model.h5) takes the real-time video from webcam as an input and predicts if the face landmarks in Region of Interest (ROI) is 'Mask' or 'No Mask' with real-time on screen accuracy.

python opencv machine-learning deep-learning scikit-learn jupyter-notebook keras-tensorflow mobilenet coronavirus face-mask-detector
  • Updated Nov 22, 2022
  • Jupyter Notebook

lai-bluejay / diego

Diego: Data in, IntElliGence Out. A fast framework that supports the rapid construction of automated learning tasks. Simply create an automated learning study (Study) and generate correlated trials (Trial). Then run the code and get a machine learning model. Implemented using Scikit-learn API glossary, using Bayesian optimization and genetic algorithms for automated machine learning. Inspired by [Fast.ai](https://github.com/fastai/fastai).

machine-learning scikit-learn hyperparameter-optimization bayesian-optimization automl autosklearn generation-algorithms
  • Updated Feb 3, 2021
  • Python

SandeepAswathnarayana / professional-certificate-programs

This repository contains all the projects and labs I worked on while pursuing professional certificate programs, specializations, and bootcamp. [Areas: Deep Learning, Machine Learning, Applied Data Science].

python tensorflow numpy scikit-learn pandas pytorch seaborn recurrent-neural-networks datascience scipy autoencoder image-classification restricted-boltzmann-machine matplotlib machinelearning recommender-system deeplearning ggplot apachespark folium-maps
  • Updated Oct 13, 2020
  • Jupyter Notebook
DataCamp-Courses--Notes-Exercises-Projects

azminewasi / DataCamp-Courses--Notes-Exercises-Projects

65+ DataCamp Course Notes, Projects, Codes, Exercises on Python, R and SQL with full DS & ML Certification,

python data-science machine-learning r sql tensorflow numpy scikit-learn machine-learning-algorithms postgresql keras pandas seaborn datascience data-analysis machine-learning-library matplotlib data-science-portfolio datacamp datascience-machinelearning
  • Updated Nov 27, 2022
  • Jupyter Notebook

rkshiyaniya / Data-Science-Machine-Learning-Projects

This Repository Contains different Machine Learning Projects on various dataset. From Exploratory Data Analysis - Visualization to Prediction and Classification..

visualization machine-learning numpy scikit-learn exploratory-data-analysis pandas python3 seaborn kaggle-competition matplotlib clustering-algorithm red-wine-quality classification-algorithm regression-algorithms k-means-clustering customer-segmentation breast-cancer-classification mcdonalds-menu
  • Updated May 28, 2021
  • Jupyter Notebook

Hameem1 / Step-Detection-using-Machine-Learning

Implements an entire machine learning pipeline to train and evaluate a Random Forest Classifier on labeled gait data for walking. Data generated during the experiment has led to helpful insights in to the problem domain.

python machine-learning random-forest scikit-learn data-visualization feature-selection feature-extraction classification supervised-machine-learning model-training step-detection gait-analysis rfe rfecv data-pre-processing step-counting model-testing
  • Updated Jul 7, 2022
  • Python

MattiaRaffa / RF-VDA-landslide-map

Modello Random Forest per la creazione di una mappa di suscettibilità da frane superficiali // // Tesi di Laurea Magistrale in Scienze della Terra (Geologia Applicata) - Università degli Studi di Milano

python tree random-forest mapping random scikit-learn forest aspect gis geology scikitlearn-machine-learning landslide geologia geologic-mapping landslide-susceptibility-mapping susceptibility randomforestclassifier applied-geology oob-error geologia-applicata
  • Updated Apr 28, 2021
  • Python

naveen-chauhan / Loan-Prediction-Classification

A Classification Problem which predicts if a loan will get approved or not.

python machine-learning scikit-learn machine-learning-algorithms jupyter-notebook python3 machinelearning jupyter-notebooks scikitlearn-machine-learning loan-default-prediction machine-learning-tutorials loan-prediction-analysis loan-application machinelearning-python machinelearningprojects loan-prediction-problem machine-learning- loan-prediction matpolotlib
  • Updated Aug 4, 2022
  • Jupyter Notebook

ankitAMD / NYTaxi_XG_Boost_Challenge-kaggle_challenge

I'm attempting the NYC Taxi Duration prediction Kaggle challenge. I'll by using a combination of Pandas, Matplotlib, and XGBoost as python libraries to help me understand and analyze the taxi dataset that Kaggle provides. The goal will be to build a predictive model for taxi duration time. I'll also be using Google Colab as my jupyter notebook. i will also predict without Google colab on normal system.

python machine-learning deep-learning random-forest numpy linear-regression scikit-learn pandas python3 kaggle-competition xgboost matplotlib feature-engineering knn-regression xgboost-model nyc-taxi-dataset fastest-routes visualization-image google-colab remote-server
  • Updated Sep 21, 2018
  • Jupyter Notebook

dgrubis / NBA-Draft-Model-2018

Jupyter notebook that outlines the process of creating a machine learning predictive model. Predicts the peak "Wins Shared" by the current draft prospects based on numerous features such as college stats, projected draft pick, physical profile and age. I try out multiple models and pick the best performing one for the data from my judgement.

nba machine-learning neural-network linear-regression scikit-learn ridge-regression multi-layer-perceptron nba-analytics prospects scikitlearn-machine-learning nba-prediction lasso-regression support-vector-regression regression-algorithms college-basketball
  • Updated Apr 17, 2018
  • Jupyter Notebook

omonimus1 / super-computer-vision

Sponsor

Notes about Computer vision and implementation of image-processing, face-detection, face-recognition, and character optical recognition applications.

python learning computer-vision numpy scikit-learn image-processing pillow edge face-recognition face-detection filters learning-by-doing cv2 opencv-python skimage face-detection-using-opencv computer-vison image-processing-python opencv-notes computer-vision-note
  • Updated Sep 5, 2022
  • Python

krunal-nagda / Credit-Card-Fraud-Detection-Capstone-Project---Decision-Tree-and-Random-Forest

In the banking industry, detecting credit card fraud using machine learning is not just a trend; it is a necessity for banks, as they need to put proactive monitoring and fraud prevention mechanisms in place. Machine learning helps these institutions reduce time-consuming manual reviews, costly chargebacks and fees, and denial of legitimate transactions. Suppose you are part of the analytics team working on a fraud detection model and its cost-benefit analysis. You need to develop a machine learning model to detect fraudulent transactions based on the historical transactional data of customers with a pool of merchants.

machine-learning numpy scikit-learn exploratory-data-analysis pandas python3 seaborn decision-tree-classifier random-forest-classifier matplotlib-pyplot
  • Updated May 21, 2021
  • Jupyter Notebook

ragamarkely / enron_fraud

Build a model to predict employees involved in Enron fraud case based on email & financial data set. Use feature selection & engineering, algorithm selection, & model selection based on F1 score, precision, & recall.

python machine-learning random-forest scikit-learn naive-bayes-classifier classification logistic-regression adaboost support-vector-machines knn multi-layer-perceptron knn-classification
  • Updated Sep 19, 2017
  • Jupyter Notebook

DavidRavnsborg / Predictive-Modeling-on-Gait-Data

Final results and Python code for an experiment applying predictive modeling, using LSTMs, to time-series gait data. The "Resampling and Epochs test" shows the results of the first iteration of optimizing model parameters two at a time. "Batch Size and Neurons test" shows the results of the second test, optimizing the remaining two parameters.

python numpy scikit-learn keras pandas lstm matplotlib
  • Updated Sep 26, 2017
  • Python

badl7 / Forecasting_future_sales

Forecasting future sales of a product offers many advantages. Predicting future sales of a product helps a company manage the cost of manufacturing and marketing the product. In this notebook, I will try to you through the task of future sales prediction with machine learning using Python.

python data-science machine-learning timeseries scikit-learn keras regression lstm xgboost arima statsmodels sarimax arima-forecasting
  • Updated Sep 29, 2022
  • Jupyter Notebook

d-wasserman / geo-learn

This is a collection scripts and tools intended to provide a template on how to integrate and apply Scikit-Learn with ArcGIS Pro. The tools distributed enable access to various machine learning algorithms through scripting tools in the geo-learn toolbox. The tools largely work by passing geographic coordinates and related data to be clustered or analyzed to help with spatial analysis tasks, data reduction, or cartography. In addition, the tool sets include regression analysis tools for exploring different Scikit-Learn model's ability to provide predictive analysis.

arcgis analysis clustering scikit-learn gis spatial-analysis-tasks
  • Updated Apr 6, 2020
  • Python

sarincr / Machine-Learning-Python-Bootcamp

Basic exercises on Machine Learning with Python , Reference Book : Python for Probability, Statistics, and Machine Learning by José Unpingco

python machine-learning statistics deep-learning neural-network algorithms numpy scikit-learn sklearn machine-learning-algorithms regression pandas python3 artificial-intelligence scipy logistic-regression matplotlib machinelearning support-vector-machine machinelearning-python
  • Updated Mar 27, 2022
  • Jupyter Notebook

Created by David Cournapeau

Released January 05, 2010

Latest release about 1 month ago

Repository
scikit-learn/scikit-learn
Website
scikit-learn.org
Wikipedia
Wikipedia

Related Topics

python scikit